A fundamental problem of semantics is the grounding problem [1]}, which concerns the circularity in defining the meaning of a symbol through other symbols. In the context of Searle's famous Chinese Room argument [2]}, it can be phrased as: is it possible to learn Chinese from nothing but a (very sophisticated) Chinese dictionary? Modern representation learning approaches, including the word embeddings that have become popular in natural language processing, are exponents of the distributional hypothesis [3]}, which stipulates that you “shall know the meaning of a word through the company that it keeps” [4]}. In other words, semantic representation learning defines symbols through other symbols, which exposes it to the grounding problem. In contrast, there is abundant evidence that human meaning representation is grounded in physical reality and sensorimotor experience [5]}, [6]}, [7]}, [8]}, [9]}.
